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 *    notice, this list of conditions and the following disclaimer.
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 *    notice, this list of conditions and the following disclaimer in the
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/* Vector addition: C = A + B.
 *
 * This sample is a very basic sample that implements element by element
 * vector addition. It is the same as the sample illustrating Chapter 3
 * of the programming guide with some additions like error checking.
 *
 */

// Includes
#include <stdio.h>
#include <string.h>
#include <iostream>
#include <cstring>
#include <cuda.h>

// includes, project
#include <helper_cuda_drvapi.h>
#include <helper_functions.h>

// includes, CUDA
#include <builtin_types.h>

using namespace std;

// Variables
CUdevice cuDevice;
CUcontext cuContext;
CUmodule cuModule;
CUfunction vecAdd_kernel;
float *h_A;
float *h_B;
float *h_C;
CUdeviceptr d_A;
CUdeviceptr d_B;
CUdeviceptr d_C;

// Functions
int CleanupNoFailure();
void RandomInit(float *, int);
bool findModulePath(const char *, string &, char **, string &);

// define input fatbin file
#ifndef FATBIN_FILE
#define FATBIN_FILE "vectorAdd_kernel64.fatbin"
#endif

// Host code
int main(int argc, char **argv) {
  printf("Vector Addition (Driver API)\n");
  int N = 50000, devID = 0;
  size_t size = N * sizeof(float);

  // Initialize
  checkCudaErrors(cuInit(0));

  cuDevice = findCudaDeviceDRV(argc, (const char **)argv);
  // Create context
  checkCudaErrors(cuCtxCreate(&cuContext, 0, cuDevice));

  // first search for the module path before we load the results
  string module_path;

  std::ostringstream fatbin;

  if (!findFatbinPath(FATBIN_FILE, module_path, argv, fatbin)) {
    exit(EXIT_FAILURE);
  } else {
    printf("> initCUDA loading module: <%s>\n", module_path.c_str());
  }

  if (!fatbin.str().size()) {
    printf("fatbin file empty. exiting..\n");
    exit(EXIT_FAILURE);
  }

  // Create module from binary file (FATBIN)
  checkCudaErrors(cuModuleLoadData(&cuModule, fatbin.str().c_str()));

  // Get function handle from module
  checkCudaErrors(
      cuModuleGetFunction(&vecAdd_kernel, cuModule, "VecAdd_kernel"));

  // Allocate input vectors h_A and h_B in host memory
  h_A = (float *)malloc(size);
  h_B = (float *)malloc(size);
  h_C = (float *)malloc(size);

  // Initialize input vectors
  RandomInit(h_A, N);
  RandomInit(h_B, N);

  // Allocate vectors in device memory
  checkCudaErrors(cuMemAlloc(&d_A, size));

  checkCudaErrors(cuMemAlloc(&d_B, size));

  checkCudaErrors(cuMemAlloc(&d_C, size));

  // Copy vectors from host memory to device memory
  checkCudaErrors(cuMemcpyHtoD(d_A, h_A, size));

  checkCudaErrors(cuMemcpyHtoD(d_B, h_B, size));

  if (1) {
    // This is the new CUDA 4.0 API for Kernel Parameter Passing and Kernel
    // Launch (simpler method)

    // Grid/Block configuration
    int threadsPerBlock = 256;
    int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;

    void *args[] = {&d_A, &d_B, &d_C, &N};

    // Launch the CUDA kernel
    checkCudaErrors(cuLaunchKernel(vecAdd_kernel, blocksPerGrid, 1, 1,
                                   threadsPerBlock, 1, 1, 0, NULL, args, NULL));
  } else {
    // This is the new CUDA 4.0 API for Kernel Parameter Passing and Kernel
    // Launch (advanced method)
    int offset = 0;
    void *argBuffer[16];
    *((CUdeviceptr *)&argBuffer[offset]) = d_A;
    offset += sizeof(d_A);
    *((CUdeviceptr *)&argBuffer[offset]) = d_B;
    offset += sizeof(d_B);
    *((CUdeviceptr *)&argBuffer[offset]) = d_C;
    offset += sizeof(d_C);
    *((int *)&argBuffer[offset]) = N;
    offset += sizeof(N);

    // Grid/Block configuration
    int threadsPerBlock = 256;
    int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
    void *kernel_launch_config[5] = {CU_LAUNCH_PARAM_BUFFER_POINTER,
                                     argBuffer, CU_LAUNCH_PARAM_BUFFER_SIZE,
                                     &offset, CU_LAUNCH_PARAM_END};
    // Launch the CUDA kernel
    checkCudaErrors(cuLaunchKernel(vecAdd_kernel, blocksPerGrid, 1, 1,
                                   threadsPerBlock, 1, 1, 0, NULL, NULL,
                                   kernel_launch_config));
  }

#ifdef _DEBUG
  checkCudaErrors(cuCtxSynchronize());
#endif

  // Copy result from device memory to host memory
  // h_C contains the result in host memory
  checkCudaErrors(cuMemcpyDtoH(h_C, d_C, size));

  // Verify result
  int i;

  for (i = 0; i < N; ++i) {
    float sum = h_A[i] + h_B[i];

    if (fabs(h_C[i] - sum) > 1e-7f) {
      break;
    }
  }

  CleanupNoFailure();
  printf("%s\n", (i == N) ? "Result = PASS" : "Result = FAIL");

  exit((i == N) ? EXIT_SUCCESS : EXIT_FAILURE);
}

int CleanupNoFailure() {
  // Free device memory
  checkCudaErrors(cuMemFree(d_A));
  checkCudaErrors(cuMemFree(d_B));
  checkCudaErrors(cuMemFree(d_C));

  // Free host memory
  if (h_A) {
    free(h_A);
  }

  if (h_B) {
    free(h_B);
  }

  if (h_C) {
    free(h_C);
  }

  checkCudaErrors(cuCtxDestroy(cuContext));

  return EXIT_SUCCESS;
}
// Allocates an array with random float entries.
void RandomInit(float *data, int n) {
  for (int i = 0; i < n; ++i) {
    data[i] = rand() / (float)RAND_MAX;
  }
}